If you are thinking about learning Python and doing some research – you may be asking about: What is Python used for? or What are use cases of Python in today’s world?
In today’s article, we will explore the top 5 Python use cases which will give you an idea about where and how Python is being used.
From Developers to data scientists, from developing a simple web application to data analysis and visualization, Python is one of the most popular and favorite programming language choices as it can be used in the diverse set of scope.
Powerful Libraries, built-in data structures, Strong community, and various frameworks make it the strong choice, fast-growing among the developers over other programming languages.
Being in the market as one of the most emerged languages for over thirty years, Python is now becoming the favorite among developers to build a scripting solution for sysadmins to web application development for developers to the driving force behind machine learning.
In this, article we will explain why Python is emerging as the most reliable programming language and discuss the top five use cases of Python, which will serve as a strong foundation and proven choice for developing your application.
1. Python For Web Development
Python is an excellent choice for web development due to its simple syntax, robust ecosystem, and readily available frameworks.
Building a web application using python means writing code that runs on the server for the data transfer from and to the server, managing the user’s authentication, assurance of the security and stability of the system, and much more.
Many of the frameworks are build covering all these scopes; hence we can jump straightway into developing features for our application.
– Easy Syntax
Yes, you heard it right, Python is relatively an easy language to learn, and most developers agree on that. Python is easy to learn in terms of the syntax due to its easily readable code.
Python syntax is easy, simple, and clean to grasp and understand
– Various Frameworks
The availability of the various frameworks makes things a lot easier for developers. Availability of the strong foundation, extensive set of the libraries, frameworks cuts the development time and allows developers to jump into the application development.
Python being one of the most popular programming languages; there are many frameworks, and developers can choose at their ease. Different frameworks had their own merits and demerits. Hence, developers need to be aware of choosing the right one on the basis of project requirements.
Django, Flask, Tornado, Web2py, Bottle, CherryPy, Bottle are among the most popular frameworks.
2. Python For Data Science
Data Science has gained a lot of popularity in the last few years. Data science’s primary focus is extrapolating useful information from massive stores of statistics and data, which facilitates in making better strategies in marketing, business development, and help make better decisions.
There are various tools and technologies available for data analytics, such as Hadoop, R programming, SAS, SQL, etc. However, the most popular and easy to use tools for data analytics in Python.
Python has a unique attribute and is easy to use when it comes to quantitative and analytical computing. It has been the industry leader for quite some time now and is being widely used in finance, health care, Internet search, eCommerce, and others.
Python is powerful and easy to use, has massive libraries from data manipulation and analysis, proven for its scalability and performance, and had many packages for visualization and graphics, which makes it most desirable for data science.
3. Python in AI and Machine Learning
Machine Learning and Artificial intelligence-based projects are having a profound effect on the world we live in, with new innovations emerging to solve real-world problems.
“[AI] is going to change the world more than anything in the history of mankind. More than electricity.”— AI oracle and venture capitalist Dr. Kai-Fu Lee,
From playing chess to using self-driving cars and from using social media to eCommerce, we all want better recommendation systems, improved search functionality, personalized experience, virtual assistant’s bots, fraud detection systems, which all are made possible by AI and machine learning.
To implement your AI aspirations, your choice of the programming should be stable, flexible, and has tools available simplicity and consistency, access to great frameworks, libraries, and extensions that simplify the implementation of different functionalities.
Python offers all of this, which is why it has more edge over other programming languages for AI projects today.
Below are the most commonly used Framework and Libraries for AI & ML based projects:
| Data analysis and visualization | NumPy, SciPy, Pandas, Seaborn |
| Machine learning | TensorFlow, Keras, Scikit-learn |
| Computer vision | OpenCV |
| Natural language processing | NLTK, spaCy |
Although other programming languages can also be used in AI projects, the fact that Python is at the cutting edge and should be given significant attention does not get away.
4. Python For Startups
Have you got a brilliant idea for your startup and want to develop it?
The first thing you need to decide is to choose the right programming language, but it is not an easy decision as there are many options to choose from.
The main goal of a startup business is to get a minimum viable product (MVP) to the market fit as quickly as possible.
Your choice of programming language for a startup is a foundation of your product, and you should assure it have better performance, stability, stability, better user experience for future growth.
As a startup, the programming language you choose must address the following things:
- The quick development of Minimum Viable ProductFast iteration
- Easy to integrate with third-party software
- High scope of Scalability
- Easy implementation of new features
- easiness in finding product support
Python actually fulfills all these demands and needs of a startup to emerge as an exemplary choice. Python is one of the programming languages designed for rapid application development and definitely may help you prepare rapid prototypes.
Availability of the libraries you could possibly imagine, a global community and enormous support network, user-friendly, versatile, emerging programming, scalable and stable, all these features make Python one of the very best options out there.
Python is one of the oldest programming languages, but still, one of the emerging in today’s date means it’s time-tested, but not yet outdated and growing every day.
Tech giants like Google, Facebook, Instagram, and Netflix all have major dependencies on Python. Check out Top 10 companies using Python.
All these features make Python one of the best programming languages for startups.
5. Python in Fintech
Matching business objectives with the right tech stack is the key to success when it comes to Fintech scalability, high-performance, maturity, security, etc. are most. On the other hand, being a part of the ambitious ventures i.e., the FinTech industry, these companies need to make development decisions based on other factors like API connections with other applications, compatible with other software, and safety.
With so many aspects to be considered, the choice of the right programming language may look like an impossible task. Luckily, Python is there to answer all your Fintech needs.
Python is a leading programming language in FinTech, and is also widely used in the finance, insurance and data analytics sector. It brings with the tools to do market research, analysis, and prediction, most for the Fintech applications.
That why’s Python rules the finance scene with its simplicity in development and practicality in creating algorithms and formulas. Python is also becoming popular as a tool to build Blockchain and crypto currency-based applications.
According to the HackerRank 2018 Developer Skills Report, Python is among the top three most popular languages in financial services. In 2020 the situation hasn’t changed much, Python still appears to be one of the most wanted languages in the bank industry.
With the abundance of supporting libraries, Python also seems to have answers to most challenges raised by the financial industry when looking at analytics, regulation, compliance, and data.
So, what to expect more? A technology is sturdy enough to withstand the stress of worldwide financial but still flexible enough to adapt to the challenges of the new world and the customers’ growing needs. Python has it all that is required for Fintech.